This study attempts to delineate a correlation between personality traits and academic achievement. Education as a significant contributor to sustainable economic development, academic achievement across the board is a key indicator of educational quality. Therefore this represents a criteria worthy for consideration by policy managers and advisors to make informed decisions that secure economic prosperity. Personality, as a distinctive quality amongst students, has therefore compelled the undertaking for further research and thus the culmination of this report. Data related to our findings was acquired through a self-assessed survey, where participants answered questions pertaining to specific personal attributes. ATAR results were also recorded so to quantify the academic achievement of respective candidates. Questions were then categorised by their respective personality trait - creativity, perseverance, and social intelligence - each addressed as a research question for further analysis.
In 2014, the Australian government spent $13.8 billion on schools (APH, 2017), and, as of 2016, 24.3% of the Australian population had completed a bachelor degree or higher (ABS, 2016). On an economic level, education is a key concern in fostering sustainable economic development. The improvement of academic achievement amongst students is a fundamental premise in educational policies and management and aids policy advisors in understanding educational quality. Although determining the contributing factors of high academic achievement would be incredibly valuable for educational planning, various factors obscure the relationship. With this in mind, we created a survey to measure three qualitative variables based on personality type: creativity, perseverance, and social intelligence, and two quantitative variables that requested for expected and actual ATAR (survey can be found here: https://docs.google.com/forms/d/e/1FAIpQLSdxSCEJCBQ1hf6ORLbvaPb58XVc3LMpTqgXpW_xdFFDg3i8mQ/viewform).
Measuring personalities with a self-assessed survey presents a number of challenges, as personality traits cannot be objectively justified. This is due to the subjective nature of the study where varying degrees of response bias can occur amongst candidates. Therefore, the results potentially could pose inaccurate and/or misleading insights. Given the scope of the assessment, it is difficult to address limitations effectively e.g. small question set, bias, small sample size. We attempted to compensate for observer bias using questions that did not directly inform the candidate of the personality being assessed to prevent subjects from being aware of the intentions of the study e.g. “I believe it is best to forgive and forget” assessing perseverance. However, this raises the question of the ethics of our survey, because subjects were unaware of how their response would be used. Additionally, selection bias was present in the survey due to the nature of how our survey was distributed on social media and the breath of educational status that was analysed. This only considered individuals who have completed their high school certificate and those being a majority from a sydney demographic. Moreover, ATAR being considered quantitative is worth being noted of as distinguishing the variable between being either quantitative or qualitative is a grey area. ATAR being a highly precise numerical rank ranging from 0-99.95, its relatively discrete measure of 0.05 increments validates its consideration as a quantitative variable.
Stakeholders with an interest in this report include:
The education sector - accounting for the differences that personality traits may cause, educators can take necessary measures to recognise their students’ abilities and address their individual needs to maximise student potential and academic achievement.
Governments have an obligation to efficiently and effectively address the needs of their people. Analysing and understanding the personality traits of students, resources can be allocated most appropriately to address student needs on a nationwide scale.
library(plotly)## Warning: package 'ggplot2' was built under R version 3.5.3# LOAD DATA v1 - uncomment the link below to: load data direct from html
setwd("C:/Users/anosh/Documents/Anosh's Data/!_Uni_!/DATA1001 - Intro to Data/Assignment 2")
resc = read.csv("Data/Responses.csv")
# Quick look at top 5 rows of data
head(resc, n=1)## I.believe.it.is.best.to.forgive.and.forget.
## 1 Like me
## Being.able.to.come.up.with.new.and.different.ideas.is.one.of.my.strong.points.
## 1 Very like me
## My.friends.say.that.I.have.lots.of.new.and.different.ideas.
## 1 Very like me
## I.know.how.to.handle.myself.in.different.social.situations.
## 1 Very like me
## I.enjoy.being.kind.to.others.
## 1 Like me
## I.finish.things.despite.obstacles.in.the.way.
## 1 Very like me
## I.rarely.call.attention.to.myself.
## 1 Unlike me
## I.like.to.think.of.new.ways.to.do.things.
## 1 Very like me
## I.know.that.I.will.succeed.with.the.goals.I.set.for.myself.
## 1 Very like me
## What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
## 1 85
## What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
## 1 70## Size of data
dim(resc)## [1] 48 11As indicated above, 48 candidates participated in the survey answering a total of 11 questions.
## R's classification of data
class(resc)## [1] "data.frame"## R's classification of variables
str(resc)## 'data.frame': 48 obs. of 11 variables:
## $ I.believe.it.is.best.to.forgive.and.forget. : Factor w/ 4 levels "Like me","Unlike me",..: 1 3 1 3 1 1 1 1 3 2 ...
## $ Being.able.to.come.up.with.new.and.different.ideas.is.one.of.my.strong.points. : Factor w/ 4 levels "Like me","Unlike me",..: 3 2 3 1 2 3 1 3 3 1 ...
## $ My.friends.say.that.I.have.lots.of.new.and.different.ideas. : Factor w/ 4 levels "Like me","Unlike me",..: 3 2 3 1 2 1 1 3 3 1 ...
## $ I.know.how.to.handle.myself.in.different.social.situations. : Factor w/ 4 levels "Like me","Unlike me",..: 3 1 1 2 1 3 1 1 3 3 ...
## $ I.enjoy.being.kind.to.others. : Factor w/ 3 levels "Like me","Unlike me",..: 1 1 3 3 3 3 3 3 3 3 ...
## $ I.finish.things.despite.obstacles.in.the.way. : Factor w/ 4 levels "Like me","Unlike me",..: 3 1 3 2 3 3 3 1 3 3 ...
## $ I.rarely.call.attention.to.myself. : Factor w/ 4 levels "Like me","Unlike me",..: 2 3 2 1 1 1 2 2 3 1 ...
## $ I.like.to.think.of.new.ways.to.do.things. : Factor w/ 4 levels "Like me","Unlike me",..: 3 2 1 1 1 2 1 1 3 1 ...
## $ I.know.that.I.will.succeed.with.the.goals.I.set.for.myself. : Factor w/ 4 levels "Like me","Unlike me",..: 3 2 3 1 1 1 3 1 3 1 ...
## $ What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.: int 85 70 94 85 90 90 86 75 99 95 ...
## $ What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value. : num 70 83 98 78 85 89 82 73 99 97.6 ...Creativity is a psychological construct, which is difficult to empirically define, since by definition creativity is a subjective measure of originality. Thus, existing literature is limited on the explicit relationship between creativity and academic performance, due to difficulty in research design. To our knowledge, Balgiu and Adir (2013) have produced the only peer reviewed research paper (in English - there is one in Portuguese) explicitly investigating the relationship between creativity and intelligence and their results found no correlation between creativity and academic performance in undergraduate students at the University Politehnica of Bucharest.
Our investigation differs from Balgiu and Adir’s, where they measured creativity through proficiency in performing creative tasks, our survey based research has attempted to measure creativity through response to psychometric questions such as “I like to think of new ways to do things”, to which a participant responds with 4 varying levels of agreement. Our basis for this method of assessing creativity was that it focuses more specifically on self assessed levels of creativity, which we believed to more accurately represent those who attempt to be creative, regardless of ability.
Our findings were promising, showing that median ATAR (since the results tended to be left skewed) typically was higher in responses that indicated a participant was more creative. Specifically, median ATAR for responses indicating the highest level of creativity were 96, 97 and 98(figures 1a, 1b, 1c respectively), respectively the highest of all creativity levels. Thus, we can see that there may be a correlational link, but have neither the magnitude of data, nor the experimental control to make any further conclusions about causation or strength of correlation. To build on these findings, a more controlled study which verified academic performance (which we could not do) and asked more questions to ascertain level of creativity (potentially allowing for a model against mean of response to be fitted) would be ideal.
library(plotly)
Creativity.measure.1 <- resc$My.friends.say.that.I.have.lots.of.new.and.different.ideas.
Creativity.measure.2 <- resc$I.like.to.think.of.new.ways.to.do.things.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orcrea1 = factor(Creativity.measure.1, levels=levels(Creativity.measure.1)[c(4,2,1,3)])
orcrea2 = factor(Creativity.measure.2, levels=levels(Creativity.measure.2)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orcrea1, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'My friends say that I have lots of new and different ideas'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.13,
y = 1.06,
size = 2,
text = "<b><i>Figure 1a<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Creative Types"
, xaxis = x, yaxis = y)
p1library(plotly)
Creativity.measure.1 <- resc$My.friends.say.that.I.have.lots.of.new.and.different.ideas.
Creativity.measure.2 <- resc$I.like.to.think.of.new.ways.to.do.things.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orcrea2 = factor(Creativity.measure.2, levels=levels(Creativity.measure.2)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orcrea2, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'I like to think of new ways to do things'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.13,
y = 1.06,
size = 2,
text = "<b><i>Figure 1b<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Creative Types"
, xaxis = x, yaxis = y)
p1library(plotly)
Creativity.measure.1 <- resc$My.friends.say.that.I.have.lots.of.new.and.different.ideas.
Creativity.measure.2 <- resc$I.like.to.think.of.new.ways.to.do.things.
Creativity.measure.3 <- resc$Being.able.to.come.up.with.new.and.different.ideas.is.one.of.my.strong.points.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orcrea3 = factor(Creativity.measure.3, levels=levels(Creativity.measure.3)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orcrea3, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'Being able to come up with new and different ideas is one of my strong points'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.13,
y = 1.06,
size = 2,
text = "<b><i>Figure 1c<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Creative Types"
, xaxis = x, yaxis = y)
p1Perseverance is persistence of doing something despite difficulty or delay in achieving success. In relation to academic performance, perseverance can be directly related to having a growth mindset where an individual’s most basic abilities can be developed through dedication and hard work. Studies exist with this focus in analysing the relationship, but conclusive evidence is lacking. Studies such as Duckworth et al., 2007; Gottfredson, 1997; Hartigan & Wigdor, 1989; Pascarella & Terenzini, 2005 have found that performance measures are indicative of an individual’s intelligence but through Robbins, Oh, Le, & Button, 2009, it is clear that the relationship is rather mediated by other situational variables outside of the students’ control such as socioeconomic status. As a result, further investigation was sought to, which led to the development of our survey with emphasis on this relationship.
With the results obtained, a boxplot for each survey question was utilised to highlight any possible correlation between academic performance and perseverance as seen in Figures 2a, 2b and 2c. Through an examination of each factor of perseverance, the inconsistency of the medians of all the qualitative measures as well as the large spread across all the quantitative measures highlights how there is no strong correlation between perseverance and academic performance.
library(plotly)
Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orper1, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'I believe it is best to forgive and forget'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.18,
y = 1.06,
size = 2,
text = "<b><i>Figure 2a<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1library(plotly)
Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orper2, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'I finish things despite obstacles in the way'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.18,
y = 1.06,
size = 2,
text = "<b><i>Figure 2b<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1library(plotly)
Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
Perseverance.measure.3 <- resc$I.know.that.I.will.succeed.with.the.goals.I.set.for.myself.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
orper3 = factor(Perseverance.measure.3, levels=levels(Perseverance.measure.3)[c(4,2,1,3)])
x <- list(
title = "Actual ATAR"
)
y <- list(
title = ""
)
p1 <- plot_ly(resc, x = ~aATAR, color = ~orper3, type = "box", boxpoints = "all", jitter = 0.3,
pointpos = -1.8, marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1")) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.535,
y = 1.02,
size = 2,
text = "<b><i>'I know that I will succeed with the goals I set for myself'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = -0.18,
y = 1.06,
size = 2,
text = "<b><i>Figure 2c<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1However, further investigation using regression analysis, research highlights (Figures 2d, 2e and 2f) a weak correlation where those associating highly and lowly with perseverance appear spread across expected and actual ATAR. This implies that regardless of setting high expectations for high academic achievement (a quality of perseverance) candidates had varied actual results that reaffirmed the weak correlation and need to consider other confounders e.g. socioeconomic status. But for the purpose of making predictions, caution from this linear model must be taken as the weak correlation as inferred earlier does not substantiate expectations for actual academic achievement. Hence, this relationship is inconclusive and thus requires further investigation using more appropriate testing methods to ascertain more definitive results.
Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
fit <- lm(aATAR ~ eATAR, data = resc)
x <- list(
title = "Expected ATAR"
)
y <- list(
title = "Actual ATAR"
)
p1 <- plot_ly(resc, x = ~eATAR, y = ~aATAR, color = ~orper1, type = "scatter", mode = "markers",
marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1"), text = ~paste("I believe it is best to forgive and forget: ", Perseverance.measure.1, '<br>Expected ATAR:', eATAR,'<br>Actual ATAR:', aATAR)) %>%
add_lines(
x = ~eATAR,
y = fitted(fit),
showlegend = FALSE
)%>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.57,
y = 1.02,
size = 2,
text = "<b><i>'I believe it is best to forgive and forget'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 1.15,
y = 0.02,
size = 2,
text = "<b><i>Figure 2d<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR vs. Expected ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
fit <- lm(aATAR ~ eATAR, data = resc)
x <- list(
title = "Expected ATAR"
)
y <- list(
title = "Actual ATAR"
)
p1 <- plot_ly(resc, x = ~eATAR, y = ~aATAR, color = ~orper2, type = "scatter", mode = "markers",
marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1"), text = ~paste("I finish things despite obstacles in the way: ", Perseverance.measure.1, '<br>Expected ATAR:', eATAR,'<br>Actual ATAR:', aATAR)) %>%
add_lines(
x = ~eATAR,
y = fitted(fit),
showlegend = FALSE
)%>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.57,
y = 1.02,
size = 2,
text = "<b><i>'I finish things despite obstacles in the way'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 1.15,
y = 0.02,
size = 2,
text = "<b><i>Figure 2e<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR vs. Expected ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1Perseverance.measure.1 <- resc$I.believe.it.is.best.to.forgive.and.forget.
Perseverance.measure.2 <- resc$I.finish.things.despite.obstacles.in.the.way.
Perseverance.measure.3 <- resc$I.know.that.I.will.succeed.with.the.goals.I.set.for.myself.
eATAR <- resc$What.ATAR.did.you.expect.to.achieve.in.your.final.year.of.highschool...round.up.value.
aATAR <- resc$What.ATAR.did.you.actually.achieve.in.your.final.year.of.highschool...round.up.value.
orper1 = factor(Perseverance.measure.1, levels=levels(Perseverance.measure.1)[c(4,2,1,3)])
orper2 = factor(Perseverance.measure.2, levels=levels(Perseverance.measure.2)[c(4,2,1,3)])
orper3 = factor(Perseverance.measure.3, levels=levels(Perseverance.measure.3)[c(4,2,1,3)])
fit <- lm(aATAR ~ eATAR, data = resc)
x <- list(
title = "Expected ATAR"
)
y <- list(
title = "Actual ATAR"
)
p1 <- plot_ly(resc, x = ~eATAR, y = ~aATAR, color = ~orper3, type = "scatter", mode = "markers",
marker=list( size=6 , opacity=0.9), colors=c("dodgerblue4", "burlywood1"), text = ~paste("I know that I will succeed with the goals I set for myself: ", Perseverance.measure.1, '<br>Expected ATAR:', eATAR,'<br>Actual ATAR:', aATAR)) %>%
add_lines(
x = ~eATAR,
y = fitted(fit),
showlegend = FALSE
)%>%
add_annotations(
xref = "paper",
yref = "paper",
x = 0.57,
y = 1.02,
size = 2,
text = "<b><i>'I know that I will succeed with the goals I set for myself'<br>
</b></i>",
showarrow = FALSE
) %>%
add_annotations(
xref = "paper",
yref = "paper",
x = 1.15,
y = 0.02,
size = 2,
text = "<b><i>Figure 2f<br>
</b></i>",
showarrow = FALSE
) %>%
layout(
title = "Actual ATAR vs. Expected ATAR Across Different Perseverance Types"
, xaxis = x, yaxis = y)
p1Therefore, failure to discern any significant trend or relationship between personality traits and academic performance meant findings of the study could not be conclusive but only implied. Analysis produced slight or no correlation between the questioned personality traits and academic performance. However, through secondary reference this confirmed the mild to no correlation between the variables that had been discerned through first hand analysis. Given if the experiments were done with larger samples and more detailed and accurate testing methods, these could represent a significant improvement for our study.
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016. (2019). Retrieved from https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/2071.0~2016~Main%20Features~Educational%20Qualifications%20Data%20Summary%20~65
Baggiyam, D., & Pankajam, R. (2017). SOCIAL INTELLIGENCE IN RELATION TO ACADEMIC ACHIEVEMENT. International Journal Of Research - GRANTHAALAYAH, 5(3). doi: 10.5281/zenodo.545958
School education: expenditure - Parliament of Australia. (2019). Retrieved from https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliamentary_Library/pubs/rp/BudgetReview201314/SchoolExpend#_ftn4
Sternberg, R., & Kaufman, S. (2011). The Cambridge handbook of intelligence. Cambridge: Cambridge University Press.
Y Ganaie, M & Mudasir, Dr Hafiz. (2015). A Study of Social Intelligence & Academic Achievement of College Students of District Srinagar, J&K, India .. Journal of American Science. 1111. 23-27.